Recognizing the importance of deep learning in biometric recognition, an LF face recognition solution has been designed based on a VGG 2D+Disparity+Depth (VGG-D3) fused deep descriptor [1]}. The VGG-D3 description is formed by concatenating descriptions extracted from 2D images as well as disparity and depth maps using the VGG-16 descriptor trained on the VGG-Face 1 dataset [2]}. This was the first solution to adopt a fused deep CNN network to exploit the complementary LF information, including 2D texture and the corresponding disparity and depth maps. Another face recognition solution is based on a double-deep descriptor, so-called VGG + Conventional Long Short Term Memory (Conv-LSTM) [3]}. This solution exploits the multi-perspective LF information While the VGG-D3 [1]} processes only the information available in the 2D central SA image. The double-deep descriptor extracts spatio-angular LF dependencies using VGG and LSTM networks in sequence, to provide a more powerful description for face recognition. The solution in [5]} proposes three novel LSTM cell architectures to jointly learn from LF horizontal and vertical parallaxes. The three cell architectures perform: i) Gate-Level Fusion LSTM (GLF-LSTM); ii) State-Level Fusion LSTM (SLF-LSTM); and iii) Sequential Learning LSTM (SeqL-LSTM). These architectures create richer spatio-angular LF descriptions for face recognition and have been integrated into an end-to-end framework, where a VGG network feeds the LSTM networks composed of the novel LSTM cell architectures.
